Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations2.293.481
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory751.8 MiB
Average record size in memory343.7 B

Variable types

DateTime1
Numeric7
Categorical4
Text1

Alerts

brand is highly overall correlated with cat1 and 1 other fieldsHigh correlation
cat1 is highly overall correlated with brand and 1 other fieldsHigh correlation
cat2 is highly overall correlated with brand and 1 other fieldsHigh correlation
cust_request_tn is highly overall correlated with customer_id and 2 other fieldsHigh correlation
customer_id is highly overall correlated with cust_request_tn and 1 other fieldsHigh correlation
product_id is highly overall correlated with cust_request_tn and 2 other fieldsHigh correlation
sku_size is highly overall correlated with product_idHigh correlation
tn is highly overall correlated with cust_request_tn and 2 other fieldsHigh correlation
plan_precios_cuidados is highly imbalanced (90.5%) Imbalance
cust_request_tn is highly skewed (γ1 = 37.72862987) Skewed
tn is highly skewed (γ1 = 37.9431848) Skewed
stock_final has 1353142 (59.0%) zeros Zeros

Reproduction

Analysis started2025-06-01 21:14:09.681442
Analysis finished2025-06-01 21:15:34.386708
Duration1 minute and 24.71 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.5 MiB
Minimum2017-01-01 00:00:00
Maximum2019-12-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-01T18:15:35.024271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:35.645300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=36)

customer_id
Real number (ℝ)

High correlation 

Distinct597
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10172.667
Minimum10001
Maximum10637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 MiB
2025-06-01T18:15:36.282274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10007
Q110055
median10135
Q310269
95-th percentile10448
Maximum10637
Range636
Interquartile range (IQR)214

Descriptive statistics

Standard deviation142.26452
Coefficient of variation (CV)0.013984978
Kurtosis-0.2440503
Mean10172.667
Median Absolute Deviation (MAD)99
Skewness0.80407669
Sum2.3330817 × 1010
Variance20239.193
MonotonicityNot monotonic
2025-06-01T18:15:36.962129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 18327
 
0.8%
10004 18254
 
0.8%
10002 17544
 
0.8%
10007 17522
 
0.8%
10003 17440
 
0.8%
10027 16710
 
0.7%
10005 16601
 
0.7%
10018 16553
 
0.7%
10059 16058
 
0.7%
10034 14884
 
0.6%
Other values (587) 2123588
92.6%
ValueCountFrequency (%)
10001 18327
0.8%
10002 17544
0.8%
10003 17440
0.8%
10004 18254
0.8%
10005 16601
0.7%
10006 14345
0.6%
10007 17522
0.8%
10008 7776
0.3%
10009 13234
0.6%
10010 8694
0.4%
ValueCountFrequency (%)
10637 2
 
< 0.1%
10636 3
 
< 0.1%
10635 41
 
< 0.1%
10634 15
 
< 0.1%
10633 2
 
< 0.1%
10632 2
 
< 0.1%
10631 16
 
< 0.1%
10630 43
 
< 0.1%
10629 6
 
< 0.1%
10626 148
< 0.1%

product_id
Real number (ℝ)

High correlation 

Distinct780
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20389.669
Minimum20001
Maximum21276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 MiB
2025-06-01T18:15:37.642221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20001
5-th percentile20020
Q120133
median20321
Q320605
95-th percentile20967
Maximum21276
Range1275
Interquartile range (IQR)472

Descriptive statistics

Standard deviation301.12145
Coefficient of variation (CV)0.014768334
Kurtosis-0.38853705
Mean20389.669
Median Absolute Deviation (MAD)218
Skewness0.71355894
Sum4.6763319 × 1010
Variance90674.126
MonotonicityNot monotonic
2025-06-01T18:15:38.346910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20111 7973
 
0.3%
20122 7950
 
0.3%
20120 7537
 
0.3%
20326 7397
 
0.3%
20132 7199
 
0.3%
20004 7139
 
0.3%
20276 7097
 
0.3%
20058 7006
 
0.3%
20027 6964
 
0.3%
20013 6964
 
0.3%
Other values (770) 2220255
96.8%
ValueCountFrequency (%)
20001 6172
0.3%
20002 6000
0.3%
20003 6793
0.3%
20004 7139
0.3%
20005 5911
0.3%
20006 6497
0.3%
20007 6906
0.3%
20008 6453
0.3%
20009 5596
0.2%
20010 4611
0.2%
ValueCountFrequency (%)
21276 64
< 0.1%
21267 67
< 0.1%
21266 94
< 0.1%
21265 93
< 0.1%
21263 130
< 0.1%
21262 122
< 0.1%
21259 128
< 0.1%
21256 115
< 0.1%
21252 67
< 0.1%
21248 120
< 0.1%

plan_precios_cuidados
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.4 MiB
0
2265551 
1
 
27930

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2.293.481
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2265551
98.8%
1 27930
 
1.2%

Length

2025-06-01T18:15:39.062522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-01T18:15:39.534702image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2265551
98.8%
1 27930
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 2265551
98.8%
1 27930
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2293481
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2265551
98.8%
1 27930
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2293481
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2265551
98.8%
1 27930
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2293481
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2265551
98.8%
1 27930
 
1.2%

cust_request_qty
Real number (ℝ)

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1707967
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 MiB
2025-06-01T18:15:40.095155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum92
Range91
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.6562439
Coefficient of variation (CV)1.6842866
Kurtosis53.295061
Mean2.1707967
Median Absolute Deviation (MAD)0
Skewness6.3016896
Sum4978681
Variance13.368119
MonotonicityNot monotonic
2025-06-01T18:15:40.865626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1581270
68.9%
2 349298
 
15.2%
3 117208
 
5.1%
4 63774
 
2.8%
5 36187
 
1.6%
6 24521
 
1.1%
7 18116
 
0.8%
8 14343
 
0.6%
9 10938
 
0.5%
10 9153
 
0.4%
Other values (74) 68673
 
3.0%
ValueCountFrequency (%)
1 1581270
68.9%
2 349298
 
15.2%
3 117208
 
5.1%
4 63774
 
2.8%
5 36187
 
1.6%
6 24521
 
1.1%
7 18116
 
0.8%
8 14343
 
0.6%
9 10938
 
0.5%
10 9153
 
0.4%
ValueCountFrequency (%)
92 1
< 0.1%
90 1
< 0.1%
88 1
< 0.1%
85 2
< 0.1%
84 1
< 0.1%
83 1
< 0.1%
79 1
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%

cust_request_tn
Real number (ℝ)

High correlation  Skewed 

Distinct92001
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50052053
Minimum0.0001
Maximum551.56137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 MiB
2025-06-01T18:15:41.545851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.00197
Q10.01027
median0.04046
Q30.16528
95-th percentile1.64433
Maximum551.56137
Range551.56127
Interquartile range (IQR)0.15501

Descriptive statistics

Standard deviation3.5233851
Coefficient of variation (CV)7.0394417
Kurtosis2676.5649
Mean0.50052053
Median Absolute Deviation (MAD)0.03614
Skewness37.72863
Sum1147934.3
Variance12.414242
MonotonicityNot monotonic
2025-06-01T18:15:42.241967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01638 13925
 
0.6%
0.00218 12909
 
0.6%
0.00983 11889
 
0.5%
0.01092 10709
 
0.5%
0.00546 10651
 
0.5%
0.04095 10575
 
0.5%
0.00109 10240
 
0.4%
0.03276 10179
 
0.4%
0.00491 10163
 
0.4%
0.00819 9804
 
0.4%
Other values (91991) 2182437
95.2%
ValueCountFrequency (%)
0.0001 170
 
< 0.1%
0.00013 79
 
< 0.1%
0.00018 159
 
< 0.1%
0.0002 238
 
< 0.1%
0.00021 628
< 0.1%
0.00023 744
< 0.1%
0.00025 299
< 0.1%
0.00026 217
 
< 0.1%
0.00029 137
 
< 0.1%
0.0003 211
 
< 0.1%
ValueCountFrequency (%)
551.56137 1
< 0.1%
510.65893 1
< 0.1%
444.41192 1
< 0.1%
439.90647 1
< 0.1%
437.37767 1
< 0.1%
416.64823 1
< 0.1%
407.02225 1
< 0.1%
393.26092 1
< 0.1%
389.02653 1
< 0.1%
384.82574 1
< 0.1%

tn
Real number (ℝ)

High correlation  Skewed 

Distinct91942
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48947657
Minimum0.0001
Maximum547.87849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 MiB
2025-06-01T18:15:42.936112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.00197
Q10.01027
median0.04043
Q30.16474
95-th percentile1.638
Maximum547.87849
Range547.87839
Interquartile range (IQR)0.15447

Descriptive statistics

Standard deviation3.3959859
Coefficient of variation (CV)6.9379948
Kurtosis2740.3007
Mean0.48947657
Median Absolute Deviation (MAD)0.03611
Skewness37.943185
Sum1122605.2
Variance11.53272
MonotonicityNot monotonic
2025-06-01T18:15:43.588578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01638 13930
 
0.6%
0.00218 12908
 
0.6%
0.00983 11889
 
0.5%
0.01092 10714
 
0.5%
0.00546 10653
 
0.5%
0.04095 10575
 
0.5%
0.00109 10242
 
0.4%
0.03276 10193
 
0.4%
0.00491 10162
 
0.4%
0.00819 9811
 
0.4%
Other values (91932) 2182404
95.2%
ValueCountFrequency (%)
0.0001 170
 
< 0.1%
0.00013 79
 
< 0.1%
0.00018 159
 
< 0.1%
0.0002 238
 
< 0.1%
0.00021 628
< 0.1%
0.00023 746
< 0.1%
0.00025 299
< 0.1%
0.00026 217
 
< 0.1%
0.00029 137
 
< 0.1%
0.0003 211
 
< 0.1%
ValueCountFrequency (%)
547.87849 1
< 0.1%
469.45761 1
< 0.1%
439.90647 1
< 0.1%
437.37767 1
< 0.1%
430.90803 1
< 0.1%
414.05146 1
< 0.1%
389.02653 1
< 0.1%
386.60688 1
< 0.1%
384.82574 1
< 0.1%
379.4427 1
< 0.1%

cat1
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size112.8 MiB
PC
1275882 
HC
571463 
FOODS
442263 
REF
 
3873

Length

Max length5
Median length2
Mean length2.5801932
Min length2

Characters and Unicode

Total characters5.917.624
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHC
2nd rowHC
3rd rowHC
4th rowHC
5th rowHC

Common Values

ValueCountFrequency (%)
PC 1275882
55.6%
HC 571463
24.9%
FOODS 442263
 
19.3%
REF 3873
 
0.2%

Length

2025-06-01T18:15:44.239573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-01T18:15:44.718908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
pc 1275882
55.6%
hc 571463
24.9%
foods 442263
 
19.3%
ref 3873
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 1847345
31.2%
P 1275882
21.6%
O 884526
14.9%
H 571463
 
9.7%
F 446136
 
7.5%
D 442263
 
7.5%
S 442263
 
7.5%
R 3873
 
0.1%
E 3873
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5917624
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 1847345
31.2%
P 1275882
21.6%
O 884526
14.9%
H 571463
 
9.7%
F 446136
 
7.5%
D 442263
 
7.5%
S 442263
 
7.5%
R 3873
 
0.1%
E 3873
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5917624
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 1847345
31.2%
P 1275882
21.6%
O 884526
14.9%
H 571463
 
9.7%
F 446136
 
7.5%
D 442263
 
7.5%
S 442263
 
7.5%
R 3873
 
0.1%
E 3873
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5917624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 1847345
31.2%
P 1275882
21.6%
O 884526
14.9%
H 571463
 
9.7%
F 446136
 
7.5%
D 442263
 
7.5%
S 442263
 
7.5%
R 3873
 
0.1%
E 3873
 
0.1%

cat2
Categorical

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.8 MiB
CABELLO
610741 
DEOS
430306 
SOPAS Y CALDOS
262523 
HOGAR
198987 
ROPA LAVADO
172212 
Other values (10)
618712 

Length

Max length19
Median length14
Mean length7.58106
Min length2

Characters and Unicode

Total characters17.387.017
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVAJILLA
2nd rowVAJILLA
3rd rowVAJILLA
4th rowVAJILLA
5th rowVAJILLA

Common Values

ValueCountFrequency (%)
CABELLO 610741
26.6%
DEOS 430306
18.8%
SOPAS Y CALDOS 262523
11.4%
HOGAR 198987
 
8.7%
ROPA LAVADO 172212
 
7.5%
ADEREZOS 162986
 
7.1%
PIEL2 129877
 
5.7%
VAJILLA 121088
 
5.3%
PIEL1 72077
 
3.1%
ROPA ACONDICIONADOR 61854
 
2.7%
Other values (5) 70830
 
3.1%

Length

2025-06-01T18:15:45.280717image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cabello 610741
19.9%
deos 430306
14.0%
sopas 262523
8.6%
y 262523
8.6%
caldos 262523
8.6%
ropa 244239
 
8.0%
hogar 198987
 
6.5%
lavado 172212
 
5.6%
aderezos 162986
 
5.3%
piel2 129877
 
4.2%
Other values (8) 325849
10.6%

Most occurring characters

ValueCountFrequency (%)
O 2577885
14.8%
A 2512683
14.5%
L 2140377
12.3%
E 1612876
9.3%
S 1414937
8.1%
D 1184616
6.8%
C 1007145
 
5.8%
769285
 
4.4%
P 715865
 
4.1%
R 691969
 
4.0%
Other values (14) 2759379
15.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16415778
94.4%
Space Separator 769285
 
4.4%
Decimal Number 201954
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 2577885
15.7%
A 2512683
15.3%
L 2140377
13.0%
E 1612876
9.8%
S 1414937
8.6%
D 1184616
7.2%
C 1007145
 
6.1%
P 715865
 
4.4%
R 691969
 
4.2%
B 610741
 
3.7%
Other values (11) 1946684
11.9%
Decimal Number
ValueCountFrequency (%)
2 129877
64.3%
1 72077
35.7%
Space Separator
ValueCountFrequency (%)
769285
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16415778
94.4%
Common 971239
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 2577885
15.7%
A 2512683
15.3%
L 2140377
13.0%
E 1612876
9.8%
S 1414937
8.6%
D 1184616
7.2%
C 1007145
 
6.1%
P 715865
 
4.4%
R 691969
 
4.2%
B 610741
 
3.7%
Other values (11) 1946684
11.9%
Common
ValueCountFrequency (%)
769285
79.2%
2 129877
 
13.4%
1 72077
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17387017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 2577885
14.8%
A 2512683
14.5%
L 2140377
12.3%
E 1612876
9.3%
S 1414937
8.1%
D 1184616
6.8%
C 1007145
 
5.8%
769285
 
4.4%
P 715865
 
4.1%
R 691969
 
4.0%
Other values (14) 2759379
15.9%

cat3
Text

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 MiB
2025-06-01T18:15:45.974817image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length19
Median length16
Mean length7.7897162
Min length3

Characters and Unicode

Total characters17.865.566
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCristalino
2nd rowCristalino
3rd rowCristalino
4th rowCristalino
5th rowCristalino
ValueCountFrequency (%)
shampoo 291039
 
10.8%
aero 275284
 
10.2%
acondicionador 241799
 
9.0%
sopas 102850
 
3.8%
polvo 93755
 
3.5%
mayonesa 89442
 
3.3%
liquido 88812
 
3.3%
jabon 84291
 
3.1%
noaero 75253
 
2.8%
gel 71860
 
2.7%
Other values (80) 1278456
47.5%
2025-06-01T18:15:47.439675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1640645
 
9.2%
O 1454152
 
8.1%
A 1359671
 
7.6%
a 1154391
 
6.5%
e 901613
 
5.0%
C 792994
 
4.4%
r 777527
 
4.4%
N 631920
 
3.5%
S 603467
 
3.4%
I 588780
 
3.3%
Other values (41) 7960406
44.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8794304
49.2%
Uppercase Letter 8671902
48.5%
Space Separator 399360
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1640645
18.7%
a 1154391
13.1%
e 901613
10.3%
r 777527
8.8%
l 586840
 
6.7%
s 556288
 
6.3%
i 508443
 
5.8%
n 450559
 
5.1%
u 337542
 
3.8%
d 310292
 
3.5%
Other values (15) 1570164
17.9%
Uppercase Letter
ValueCountFrequency (%)
O 1454152
16.8%
A 1359671
15.7%
C 792994
9.1%
N 631920
7.3%
S 603467
7.0%
I 588780
6.8%
D 550293
 
6.3%
P 511109
 
5.9%
M 505849
 
5.8%
R 409804
 
4.7%
Other values (15) 1263863
14.6%
Space Separator
ValueCountFrequency (%)
399360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17466206
97.8%
Common 399360
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1640645
 
9.4%
O 1454152
 
8.3%
A 1359671
 
7.8%
a 1154391
 
6.6%
e 901613
 
5.2%
C 792994
 
4.5%
r 777527
 
4.5%
N 631920
 
3.6%
S 603467
 
3.5%
I 588780
 
3.4%
Other values (40) 7561046
43.3%
Common
ValueCountFrequency (%)
399360
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17819935
99.7%
None 45631
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1640645
 
9.2%
O 1454152
 
8.2%
A 1359671
 
7.6%
a 1154391
 
6.5%
e 901613
 
5.1%
C 792994
 
4.5%
r 777527
 
4.4%
N 631920
 
3.5%
S 603467
 
3.4%
I 588780
 
3.3%
Other values (40) 7914775
44.4%
None
ValueCountFrequency (%)
ñ 45631
100.0%

brand
Categorical

High correlation 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size121.0 MiB
NIVEA
281190 
DEOS1
275302 
SHAMPOO3
268663 
MAGGI
247823 
MUSCULO
200614 
Other values (30)
1019889 

Length

Max length9
Median length8
Mean length6.3189981
Min length3

Characters and Unicode

Total characters14.492.502
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowImportado
2nd rowImportado
3rd rowImportado
4th rowImportado
5th rowImportado

Common Values

ValueCountFrequency (%)
NIVEA 281190
12.3%
DEOS1 275302
12.0%
SHAMPOO3 268663
11.7%
MAGGI 247823
10.8%
MUSCULO 200614
 
8.7%
LIMPIEX 167424
 
7.3%
NATURA 97598
 
4.3%
SHAMPOO2 90845
 
4.0%
SHAMPOO1 81904
 
3.6%
COLBERT 66598
 
2.9%
Other values (25) 515520
22.5%

Length

2025-06-01T18:15:48.030345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nivea 281190
12.3%
deos1 275302
12.0%
shampoo3 268663
11.7%
maggi 247823
10.8%
musculo 200614
 
8.7%
limpiex 167424
 
7.3%
natura 97598
 
4.3%
shampoo2 90845
 
4.0%
shampoo1 81904
 
3.6%
colbert 66598
 
2.9%
Other values (25) 515520
22.5%

Most occurring characters

ValueCountFrequency (%)
O 1750437
12.1%
A 1596947
 
11.0%
M 1223579
 
8.4%
S 1096586
 
7.6%
I 1089783
 
7.5%
E 1079484
 
7.4%
P 708908
 
4.9%
L 598972
 
4.1%
G 570968
 
3.9%
N 569724
 
3.9%
Other values (25) 4207114
29.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13490582
93.1%
Decimal Number 886416
 
6.1%
Lowercase Letter 115504
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1750437
13.0%
A 1596947
11.8%
M 1223579
 
9.1%
S 1096586
 
8.1%
I 1089783
 
8.1%
E 1079484
 
8.0%
P 708908
 
5.3%
L 598972
 
4.4%
G 570968
 
4.2%
N 569724
 
4.2%
Other values (15) 3205194
23.8%
Lowercase Letter
ValueCountFrequency (%)
o 28876
25.0%
m 14438
12.5%
p 14438
12.5%
r 14438
12.5%
t 14438
12.5%
a 14438
12.5%
d 14438
12.5%
Decimal Number
ValueCountFrequency (%)
1 432748
48.8%
3 324153
36.6%
2 129515
 
14.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 13606086
93.9%
Common 886416
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1750437
12.9%
A 1596947
11.7%
M 1223579
 
9.0%
S 1096586
 
8.1%
I 1089783
 
8.0%
E 1079484
 
7.9%
P 708908
 
5.2%
L 598972
 
4.4%
G 570968
 
4.2%
N 569724
 
4.2%
Other values (22) 3320698
24.4%
Common
ValueCountFrequency (%)
1 432748
48.8%
3 324153
36.6%
2 129515
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14492502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1750437
12.1%
A 1596947
 
11.0%
M 1223579
 
8.4%
S 1096586
 
7.6%
I 1089783
 
7.5%
E 1079484
 
7.4%
P 708908
 
4.9%
L 598972
 
4.1%
G 570968
 
3.9%
N 569724
 
3.9%
Other values (25) 4207114
29.0%

sku_size
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean415.88843
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 MiB
2025-06-01T18:15:48.634576image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q190
median220
Q3450
95-th percentile1000
Maximum10000
Range9999
Interquartile range (IQR)360

Descriptive statistics

Standard deviation677.77956
Coefficient of variation (CV)1.6297149
Kurtosis46.201652
Mean415.88843
Median Absolute Deviation (MAD)170
Skewness5.338533
Sum9.5383221 × 108
Variance459385.14
MonotonicityNot monotonic
2025-06-01T18:15:49.259395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 223429
 
9.7%
90 156578
 
6.8%
400 148808
 
6.5%
50 136509
 
6.0%
350 123263
 
5.4%
10 107218
 
4.7%
750 103162
 
4.5%
100 95921
 
4.2%
300 78059
 
3.4%
3000 70275
 
3.1%
Other values (57) 1050259
45.8%
ValueCountFrequency (%)
1 13408
 
0.6%
2 16616
 
0.7%
3 3010
 
0.1%
4 19199
 
0.8%
5 43520
1.9%
6 14249
 
0.6%
8 13526
 
0.6%
10 107218
4.7%
12 21712
 
0.9%
15 26654
 
1.2%
ValueCountFrequency (%)
10000 2017
 
0.1%
5000 6858
 
0.3%
4000 3500
 
0.2%
3000 70275
3.1%
2000 3319
 
0.1%
1400 3906
 
0.2%
1250 3478
 
0.2%
1000 24562
 
1.1%
950 12883
 
0.6%
930 60950
2.7%

stock_final
Real number (ℝ)

Zeros 

Distinct10047
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.685998
Minimum-13.66656
Maximum1562.0245
Zeros1353142
Zeros (%)59.0%
Negative23411
Negative (%)1.0%
Memory size17.5 MiB
2025-06-01T18:15:50.187220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-13.66656
5-th percentile0
Q10
median0
Q34.82313
95-th percentile52.07557
Maximum1562.0245
Range1575.691
Interquartile range (IQR)4.82313

Descriptive statistics

Standard deviation52.754478
Coefficient of variation (CV)4.5143323
Kurtosis247.31626
Mean11.685998
Median Absolute Deviation (MAD)0
Skewness13.319384
Sum26801614
Variance2783.0349
MonotonicityNot monotonic
2025-06-01T18:15:50.984246image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1353142
59.0%
0.049 727
 
< 0.1%
3.42342 468
 
< 0.1%
0.01327 394
 
< 0.1%
7.17084 391
 
< 0.1%
0.11394 367
 
< 0.1%
0.7204 355
 
< 0.1%
0.4368 341
 
< 0.1%
10.49925 330
 
< 0.1%
0.04423 327
 
< 0.1%
Other values (10037) 936639
40.8%
ValueCountFrequency (%)
-13.66656 65
 
< 0.1%
-13.33127 196
< 0.1%
-8.19961 64
 
< 0.1%
-8.15986 86
< 0.1%
-7.7212 24
 
< 0.1%
-5.86579 65
 
< 0.1%
-5.28091 94
< 0.1%
-5.0992 51
 
< 0.1%
-4.87775 74
 
< 0.1%
-4.44673 130
< 0.1%
ValueCountFrequency (%)
1562.02448 221
< 0.1%
1284.38214 158
< 0.1%
1212.36734 158
< 0.1%
1146.09799 213
< 0.1%
1097.55623 149
< 0.1%
1057.38804 189
< 0.1%
1037.85386 186
< 0.1%
1031.01561 176
< 0.1%
978.16446 46
 
< 0.1%
916.3419 215
< 0.1%

Interactions

2025-06-01T18:15:15.372650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:14:55.998272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:14:59.236164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:02.059879image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:05.884332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:09.262371image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:12.322357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:15.806425image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:14:56.505354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:14:59.647431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:02.460677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:06.362526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:09.704379image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:12.763362image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:16.345388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:14:56.972515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:00.049407image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:02.888556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:06.960140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:10.292151image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:13.205188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:16.812336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:14:57.490484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:00.442143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:03.308187image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:07.339489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:10.712408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:13.665509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:17.280992image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:14:57.945926image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:00.846190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:04.208091image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:07.853513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:11.092690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:14.092179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:17.722128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:14:58.385112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:01.235382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:04.780999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:08.283190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:11.532672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:14.512427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:18.152523image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:14:58.826047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:01.635081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:05.336032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:08.782388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:11.919314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-06-01T18:15:14.935299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-06-01T18:15:51.505708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
brandcat1cat2cust_request_qtycust_request_tncustomer_idplan_precios_cuidadosproduct_idsku_sizestock_finaltn
brand1.0001.0000.8320.0120.0230.0390.2370.3690.2530.0980.023
cat11.0001.0001.0000.0160.0190.0540.0400.3290.2060.0780.018
cat20.8321.0001.0000.0120.0170.0380.1210.2850.4190.0760.017
cust_request_qty0.0120.0160.0121.0000.378-0.4510.003-0.0060.011-0.0100.379
cust_request_tn0.0230.0190.0170.3781.000-0.5140.000-0.6050.4790.0331.000
customer_id0.0390.0540.038-0.451-0.5141.0000.006-0.009-0.027-0.018-0.514
plan_precios_cuidados0.2370.0400.1210.0030.0000.0061.0000.0760.0160.0080.000
product_id0.3690.3290.285-0.006-0.605-0.0090.0761.000-0.598-0.036-0.605
sku_size0.2530.2060.4190.0110.479-0.0270.016-0.5981.0000.0830.479
stock_final0.0980.0780.076-0.0100.033-0.0180.008-0.0360.0831.0000.033
tn0.0230.0180.0170.3791.000-0.5140.000-0.6050.4790.0331.000

Missing values

2025-06-01T18:15:20.130671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-01T18:15:25.106245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

periodocustomer_idproduct_idplan_precios_cuidadoscust_request_qtycust_request_tntncat1cat2cat3brandsku_sizestock_final
02017-01-011023420524020.053000.05300HCVAJILLACristalinoImportado500.00.0
12017-01-011003220524010.136280.13628HCVAJILLACristalinoImportado500.00.0
22017-01-011021720524010.030280.03028HCVAJILLACristalinoImportado500.00.0
32017-01-011012520524010.022710.02271HCVAJILLACristalinoImportado500.00.0
42017-01-0110012205240111.544521.54452HCVAJILLACristalinoImportado500.00.0
52017-01-011008020524010.015140.01514HCVAJILLACristalinoImportado500.00.0
62017-01-011001520524040.106000.10600HCVAJILLACristalinoImportado500.00.0
72017-01-011006220524010.189280.18928HCVAJILLACristalinoImportado500.00.0
82017-01-011015920524030.022710.02271HCVAJILLACristalinoImportado500.00.0
92017-01-011018320524010.015140.01514HCVAJILLACristalinoImportado500.00.0
periodocustomer_idproduct_idplan_precios_cuidadoscust_request_qtycust_request_tntncat1cat2cat3brandsku_sizestock_final
22934712019-12-011002120853080.158290.15829PCCABELLOShampoo BebeNIVEA200.01.82373
22934722019-12-011009320853010.055740.05574PCCABELLOShampoo BebeNIVEA200.01.82373
22934732019-12-011000320853090.624260.62426PCCABELLOShampoo BebeNIVEA200.01.82373
22934742019-12-011036720853010.004460.00446PCCABELLOShampoo BebeNIVEA200.01.82373
22934752019-12-011027820853050.060200.06020PCCABELLOShampoo BebeNIVEA200.01.82373
22934762019-12-011010520853010.022300.02230PCCABELLOShampoo BebeNIVEA200.01.82373
22934772019-12-011009220853010.006690.00669PCCABELLOShampoo BebeNIVEA200.01.82373
22934782019-12-011000620853070.028980.02898PCCABELLOShampoo BebeNIVEA200.01.82373
22934792019-12-011001820853040.015610.01561PCCABELLOShampoo BebeNIVEA200.01.82373
22934802019-12-011002020853020.015610.01561PCCABELLOShampoo BebeNIVEA200.01.82373